Hi,
First of all, thank you for building and maintaining PyPortfolioOpt. I've really enjoyed using the library over the past year—it has been one of the main tools I've used while learning portfolio optimization, and the cookbook especially made it much easier to understand the ideas behind different optimization methods.
While working through the cookbook, I noticed that it does a fantastic job covering portfolio construction—Mean-Variance Optimization, Black-Litterman, HRP, and related topics. One thing I found myself looking for, though, was an example showing what comes after building a portfolio: evaluating it using models like CAPM and the Fama-French factor models.
Over the past year, I've been working independently on quantitative finance projects and ended up building educational notebooks covering topics such as CAPM regression, Fama-French factor modeling, portfolio evaluation, Black-Litterman, and portfolio optimization.
You can find the project here:
Quant-Portfolio Repository
I also recently published a research preprint related to portfolio optimization:
SSRN Preprint
Before I start adapting these notebooks, I wanted to ask whether this is something you think would fit the direction of the cookbook. If not, I'd still love to contribute and would be happy to work on something that would be more valuable for the project.
This would actually be my first contribution to an established open-source project. Most of my work so far has been on independent research and personal projects, so I'm still learning the open-source workflow. If you think this is a worthwhile direction, I'd be more than happy to follow the project's contribution guidelines and learn the process along the way.
Thanks again for all the work you've put into PyPortfolioOpt, and I look forward to hearing your thoughts.
Best,
Viraj Nigwekar
Hi,
First of all, thank you for building and maintaining PyPortfolioOpt. I've really enjoyed using the library over the past year—it has been one of the main tools I've used while learning portfolio optimization, and the cookbook especially made it much easier to understand the ideas behind different optimization methods.
While working through the cookbook, I noticed that it does a fantastic job covering portfolio construction—Mean-Variance Optimization, Black-Litterman, HRP, and related topics. One thing I found myself looking for, though, was an example showing what comes after building a portfolio: evaluating it using models like CAPM and the Fama-French factor models.
Over the past year, I've been working independently on quantitative finance projects and ended up building educational notebooks covering topics such as CAPM regression, Fama-French factor modeling, portfolio evaluation, Black-Litterman, and portfolio optimization.
You can find the project here:
Quant-Portfolio Repository
I also recently published a research preprint related to portfolio optimization:
SSRN Preprint
Before I start adapting these notebooks, I wanted to ask whether this is something you think would fit the direction of the cookbook. If not, I'd still love to contribute and would be happy to work on something that would be more valuable for the project.
This would actually be my first contribution to an established open-source project. Most of my work so far has been on independent research and personal projects, so I'm still learning the open-source workflow. If you think this is a worthwhile direction, I'd be more than happy to follow the project's contribution guidelines and learn the process along the way.
Thanks again for all the work you've put into PyPortfolioOpt, and I look forward to hearing your thoughts.
Best,
Viraj Nigwekar